GzuPark

GzuPark

#!/bin/bash

# Check for CUDA and try to install.
# https://gitlab.com/nvidia/cuda/blob/ubuntu16.04/9.0/base/Dockerfile

apt-get update && apt-get install -y --no-install-recommends ca-certificates apt-transport-https gnupg-curl && \
        rm -rf /var/lib/apt/lists/* && \
        NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
        NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
        apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub && \

GzuPark

#!/bin/bash

# download from a package
# https://docs.docker.com/install/linux/docker-ce/ubuntu/#install-from-a-package
# https://download.docker.com/linux/ubuntu/dists/xenial/pool/stable/amd64/
wget https://download.docker.com/linux/ubuntu/dists/xenial/pool/stable/amd64/docker-ce_18.06.0~ce~3-0~ubuntu_amd64.deb
wget https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb

sudo dpkg -i docker-ce_*.deb

GzuPark

#!/bin/bash

sudo apt-get update
sudo apt-get install curl

# latest version
# https://repo.continuum.io/archive/
curl -O https://repo.continuum.io/archive/Anaconda3-5.2.0-Linux-x86_64.sh

# recommend install directory

GzuPark

defscrollback 5000
termcapinfo xterm* ti@:te@
startup_message off
hardstatus on
hardstatus alwayslastline
hardstatus string "%{.bW}%-w%{.rW}%n*%t%{-}%+w %= %c ${USER}@%H"
bindkey -k k1 select 0
bindkey -k k2 select 1
bindkey -k k3 select 2

GzuPark

#!/bin/bash

# Check for CUDA and try to install.
# https://gitlab.com/nvidia/cuda/blob/ubuntu16.04/9.0/base/Dockerfile

apt-get update && apt-get install -y --no-install-recommends ca-certificates apt-transport-https gnupg-curl && \
    rm -rf /var/lib/apt/lists/* && \
    NVIDIA_GPGKEY_SUM=d1be581509378368edeec8c1eb2958702feedf3bc3d17011adbf24efacce4ab5 && \
    NVIDIA_GPGKEY_FPR=ae09fe4bbd223a84b2ccfce3f60f4b3d7fa2af80 && \
    apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/7fa2af80.pub && \

GzuPark

c = get_config()
c.NotebookApp.ip = '*'
c.NotebookApp.port = 8888
c.NotebookApp.open_browser = False
c.NotebookApp.token = u''

GzuPark

#!/bin/bash
echo "Kind: $1"
echo "Session: $2"
echo "Epochs Start: $3"
echo "Epochs End: $4"
echo "Account: $name"

if [ $name -eq ""]; then
  name="깃계정"
fi

GzuPark

"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

GzuPark

1. Write a SQL query that is equivalent to the following relational albebra expression.

• π_{term} (σ_{docid=2}(Documents)) U π_{term} (σ_{count=3}(Documents))
• Try both union and union all to see the difference
• Try "or" instead of "union" and compare results with using union

Answer

• input